Methods and systems for troubleshooting anomalous behavior in a data center

ABSTRACT

Methods and systems described herein are directed to troubleshooting anomalous behavior in a data center. Anomalous behavior in an object of a data center, such as a computational resource, an application, or a virtual machine (“VM”), may be related to the behavior of other objects at different hierarchies of the data center. Methods and systems provide a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem. Unexpected metrics of an object topology of the data center that correspond to the performance problem are identified. A recommendation for executing remedial measures to correct the performance problem is generated based on the unexpected metrics.

TECHNICAL FIELD

This disclosure is directed to data centers and, in particular, totroubleshooting anomalous behavior in a data center from streams ofmetric data.

BACKGROUND

Electronic computing has evolved from primitive, vacuum-tube-basedcomputer systems, initially developed during the 1940s, to modernelectronic computing systems in which large numbers of multi-processorcomputer systems, such as server computers, work stations, and otherindividual computing systems are networked together with large-capacitydata-storage devices and other electronic devices to producegeographically distributed computing systems with hundreds of thousandsof components that provide enormous computational bandwidths anddata-storage capacities. These large, distributed computing systems aretypically housed in data centers and made possible by advances incomputer networking, distributed operating systems and applications,data-storage appliances, computer hardware, and software technologies.

In recent years, data centers have grown meet the increasing demand forinformation technology (“IT”) services, such as running applications fororganizations that provide business and web services to millions ofcustomers. In order to proactively manage IT systems and services,management tools have been developed to collect metric data and processthe metric data to detect performance problems and generate alerts whenproblems arise. However, typical management tools are not able totroubleshoot the cause of many types of performance problems, whichleads to lost revenue for IT service providers when systemadministrators and application owners are forced to manuallytroubleshoot performance problems. For example, a typical managementtool generates an alert when the response time of a service to a requestfrom a client exceeds a response time threshold. As a result, systemadministrators are made aware of the problem when the alert isgenerated. But system administrators may not be able to timelytroubleshoot the delayed response time because the cause of the problemmay be the result of performance problems occurring with differenthardware and software executing in the data center. Systemadministrators and application owners seek methods and systems thattroubleshoot problems, giving system administrators and owners anopportunity to timely correct the problems.

SUMMARY

Methods and systems described herein are directed to troubleshootinganomalous behavior in a data center. Anomalous behavior in an object ofa data center, such as a computational resource, an application, or avirtual machine (“VM”), may be related to the behavior of other objectsat different hierarchies of the data center. Methods and systemsdescribed herein use correlation measures to determine whether anymetrics associated with the object exhibiting anomalous behavior arecorrelated with metrics associated with other objects of the data centerto detect correlations of the metrics for specific time ranges. Specificcombinations of pair-wise correlated metrics may be used to identifyproblems and apply appropriate remedial measures.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an architectural diagram for various types of computers.

FIG. 2 shows an Internet-connected distributed computer system.

FIG. 3 shows cloud computing.

FIG. 4 shows generalized hardware and software components of ageneral-purpose computer system.

FIGS. 5A-5B show two types of virtual machine (“VM”) and VM executionenvironments.

FIG. 6 shows an example of an open virtualization format package.

FIG. 7 shows example virtual data centers provided as an abstraction ofunderlying physical-data-center hardware components.

FIG. 8 shows virtual-machine components of a virtual-data-centermanagement server and physical servers of a physical data center.

FIG. 9 shows a cloud-director level of abstraction.

FIG. 10 shows virtual-cloud-connector nodes.

FIG. 11 shows an example server computer used to host three containers.

FIG. 12 shows an approach to implementing containers on a VM.

FIG. 13 shows an example of a virtualization layer located above aphysical data center.

FIG. 14A shows a plot of an example metric.

FIGS. 14B-14C shows an operations manager that receives metrics fromphysical and virtual resources.

FIG. 15A shows an example object topology formed from objects of acluster in a data center.

FIG. 15B shows example plots of metrics associated with two VMs and aserver computer of the cluster shown in FIG. 15A.

FIG. 16A shows an example object topology comprising three levels ofdata center objects.

FIGS. 16B-16E show examples correlation coefficients calculated for aselected metric and metrics of objects in the levels of the objecttopology shown in FIG. 16A.

FIG. 17 shows an example table of rank ordered metrics of objects of theobject topology.

FIG. 18 shows an example of reducing the list of rank ordered metrics toa list of rank ordered unexpected metrics.

FIGS. 19A-19C show an example graphical use interface (“GUI”) thatenables a user to select a metric and view correlated metrics.

FIG. 20A shows a GUI that list the unexpected metrics, associatedcorrelation coefficients, and user rating icons.

FIG. 20B shows the GUI with a rating assigned to each of the top tenranked unexpected metrics.

FIG. 20C shows unexpected metrics rank ordered according to the userratings.

FIG. 20D shows a GUI that list the highest user rated unexpected metricsand recently determined unexpected metrics.

FIG. 21 shows a table of ten frequently occurring unexpected metricsthat are correlated with the selected metric.

FIG. 22 is a flow diagram illustrating an example implementation of a“method for troubleshooting anomalous behavior in a data center.”

FIG. 23 is a flow diagram illustrating an example implementation of the“identify unexpected metrics of an object topology of the data centerthat correspond to a performance problem with executing the object”procedure performed in FIG. 22.

FIG. 24 is a flow diagram illustrating an example implementation of the“computing correlation coefficient for the selected metric and metricsof the object associated with the selected metric” procedure performedin FIG. 23.

FIG. 25 is a flow diagram illustrating an example implementation of the“identify unexpected metrics associated with the object” procedureperformed in FIG. 23.

FIG. 26 is a flow diagram illustrating an example implementation of the“rate the unexpected metrics” procedure performed in FIG. 23.

DETAILED DESCRIPTION

This disclosure presents automated methods and systems fortroubleshooting anomalous behavior in a data center. In a firstsubsection, computer hardware, complex computational systems, andvirtualization are described. Methods and systems for troubleshootinganomalous behavior in a data center based on pair-wise correlatedstreams of metric data are described below in a second subsection.

Computer Hardware, Complex Computational Systems, and Virtualization

The term “abstraction” as used to describe virtualization below is notintended to mean or suggest an abstract idea or concept. Computationalabstractions are tangible, physical interfaces that are implemented,ultimately, using physical computer hardware, data-storage devices, andcommunications systems. Instead, the term “abstraction” refers, in thecurrent discussion, to a logical level of functionality encapsulatedwithin one or more concrete, tangible, physically-implemented computersystems with defined interfaces through which electronically-encodeddata is exchanged, process execution launched, and electronic servicesare provided. Interfaces may include graphical and textual datadisplayed on physical display devices as well as computer programs androutines that control physical computer processors to carry out varioustasks and operations and that are invoked through electronicallyimplemented application programming interfaces (“APIs”) and otherelectronically implemented interfaces.

FIG. 1 shows a general architectural diagram for various types ofcomputers. Computers that receive, process, and store log messages maybe described by the general architectural diagram shown in FIG. 1, forexample. The computer system contains one or multiple central processingunits (“CPUs”) 102-105, one or more electronic memories 108interconnected with the CPUs by a CPU/memory-subsystem bus 110 ormultiple busses, a first bridge 112 that interconnects theCPU/memory-subsystem bus 110 with additional busses 114 and 116, orother types of high-speed interconnection media, including multiple,high-speed serial interconnects. These busses or serialinterconnections, in turn, connect the CPUs and memory with specializedprocessors, such as a graphics processor 118, and with one or moreadditional bridges 120, which are interconnected with high-speed seriallinks or with multiple controllers 122-127, such as controller 127, thatprovide access to various different types of mass-storage devices 128,electronic displays, input devices, and other such components,subcomponents, and computational devices. It should be noted thatcomputer-readable data-storage devices include optical andelectromagnetic disks, electronic memories, and other physicaldata-storage devices.

Of course, there are many different types of computer-systemarchitectures that differ from one another in the number of differentmemories, including different types of hierarchical cache memories, thenumber of processors and the connectivity of the processors with othersystem components, the number of internal communications busses andserial links, and in many other ways. However, computer systemsgenerally execute stored programs by fetching instructions from memoryand executing the instructions in one or more processors. Computersystems include general-purpose computer systems, such as personalcomputers (“PCs”), various types of server computers and workstations,and higher-end mainframe computers, but may also include a plethora ofvarious types of special-purpose computing devices, includingdata-storage systems, communications routers, network nodes, tabletcomputers, and mobile telephones.

FIG. 2 shows an Internet-connected distributed computer system. Ascommunications and networking technologies have evolved in capabilityand accessibility, and as the computational bandwidths, data-storagecapacities, and other capabilities and capacities of various types ofcomputer systems have steadily and rapidly increased, much of moderncomputing now generally involves large distributed systems and computersinterconnected by local networks, wide-area networks, wirelesscommunications, and the Internet. FIG. 2 shows a typical distributedsystem in which a large number of PCs 202-205, a high-end distributedmainframe system 210 with a large data-storage system 212, and a largecomputer center 214 with large numbers of rack-mounted server computersor blade servers all interconnected through various communications andnetworking systems that together comprise the Internet 216. Suchdistributed computing systems provide diverse arrays of functionalities.For example, a PC user may access hundreds of millions of different websites provided by hundreds of thousands of different web serversthroughout the world and may access high-computational-bandwidthcomputing services from remote computer facilities for running complexcomputational tasks.

Until recently, computational services were generally provided bycomputer systems and data centers purchased, configured, managed, andmaintained by service-provider organizations. For example, an e-commerceretailer generally purchased, configured, managed, and maintained a datacenter including numerous web server computers, back-end computersystems, and data-storage systems for serving web pages to remotecustomers, receiving orders through the web-page interface, processingthe orders, tracking completed orders, and other myriad different tasksassociated with an e-commerce enterprise.

FIG. 3 shows cloud computing. In the recently developed cloud-computingparadigm, computing cycles and data-storage facilities are provided toorganizations and individuals by cloud-computing providers. In addition,larger organizations may elect to establish private cloud-computingfacilities in addition to, or instead of, subscribing to computingservices provided by public cloud-computing service providers. In FIG.3, a system administrator for an organization, using a PC 302, accessesthe organization's private cloud 304 through a local network 306 andprivate-cloud interface 308 and accesses, through the Internet 310, apublic cloud 312 through a public-cloud services interface 314. Theadministrator can, in either the case of the private cloud 304 or publiccloud 312, configure virtual computer systems and even entire virtualdata centers and launch execution of application programs on the virtualcomputer systems and virtual data centers in order to carry out any ofmany different types of computational tasks. As one example, a smallorganization may configure and run a virtual data center within a publiccloud that executes web servers to provide an e-commerce interfacethrough the public cloud to remote customers of the organization, suchas a user viewing the organization's e-commerce web pages on a remoteuser system 316.

Cloud-computing facilities are intended to provide computationalbandwidth and data-storage services much as utility companies provideelectrical power and water to consumers. Cloud computing providesenormous advantages to small organizations without the devices topurchase, manage, and maintain in-house data centers. Such organizationscan dynamically add and delete virtual computer systems from theirvirtual data centers within public clouds in order to trackcomputational-bandwidth and data-storage needs, rather than purchasingsufficient computer systems within a physical data center to handle peakcomputational-bandwidth and data-storage demands. Moreover, smallorganizations can completely avoid the overhead of maintaining andmanaging physical computer systems, including hiring and periodicallyretraining information-technology specialists and continuously payingfor operating-system and database-management-system upgrades.Furthermore, cloud-computing interfaces allow for easy andstraightforward configuration of virtual computing facilities,flexibility in the types of applications and operating systems that canbe configured, and other functionalities that are useful even for ownersand administrators of private cloud-computing facilities used by asingle organization.

FIG. 4 shows generalized hardware and software components of ageneral-purpose computer system, such as a general-purpose computersystem having an architecture similar to that shown in FIG. 1. Thecomputer system 400 is often considered to include three fundamentallayers: (1) a hardware layer or level 402; (2) an operating-system layeror level 404; and (3) an application-program layer or level 406. Thehardware layer 402 includes one or more processors 408, system memory410, various different types of input-output (“I/O”) devices 410 and412, and mass-storage devices 414. Of course, the hardware level alsoincludes many other components, including power supplies, internalcommunications links and busses, specialized integrated circuits, manydifferent types of processor-controlled or microprocessor-controlledperipheral devices and controllers, and many other components. Theoperating system 404 interfaces to the hardware level 402 through alow-level operating system and hardware interface 416 generallycomprising a set of non-privileged computer instructions 418, a set ofprivileged computer instructions 420, a set of non-privileged registersand memory addresses 422, and a set of privileged registers and memoryaddresses 424. In general, the operating system exposes non-privilegedinstructions, non-privileged registers, and non-privileged memoryaddresses 426 and a system-call interface 428 as an operating-systeminterface 430 to application programs 432-436 that execute within anexecution environment provided to the application programs by theoperating system. The operating system, alone, accesses the privilegedinstructions, privileged registers, and privileged memory addresses. Byreserving access to privileged instructions, privileged registers, andprivileged memory addresses, the operating system can ensure thatapplication programs and other higher-level computational entitiescannot interfere with one another's execution and cannot change theoverall state of the computer system in ways that could deleteriouslyimpact system operation. The operating system includes many internalcomponents and modules, including a scheduler 442, memory management444, a file system 446, device drivers 448, and many other componentsand modules. To a certain degree, modern operating systems providenumerous levels of abstraction above the hardware level, includingvirtual memory, which provides to each application program and othercomputational entities a separate, large, linear memory-address spacethat is mapped by the operating system to various electronic memoriesand mass-storage devices. The scheduler orchestrates interleavedexecution of various different application programs and higher-levelcomputational entities, providing to each application program a virtual,stand-alone system devoted entirely to the application program. From theapplication program's standpoint, the application program executescontinuously without concern for the need to share processor devices andother system devices with other application programs and higher-levelcomputational entities. The device drivers abstract details ofhardware-component operation, allowing application programs to employthe system-call interface for transmitting and receiving data to andfrom communications networks, mass-storage devices, and other I/Odevices and subsystems. The file system 446 facilitates abstraction ofmass-storage-device and memory devices as a high-level, easy-to-access,file-system interface. Thus, the development and evolution of theoperating system has resulted in the generation of a type ofmulti-faceted virtual execution environment for application programs andother higher-level computational entities.

While the execution environments provided by operating systems haveproved to be an enormously successful level of abstraction withincomputer systems, the operating-system-provided level of abstraction isnonetheless associated with difficulties and challenges for developersand users of application programs and other higher-level computationalentities. One difficulty arises from the fact that there are manydifferent operating systems that run within various different types ofcomputer hardware. In many cases, popular application programs andcomputational systems are developed to run on only a subset of theavailable operating systems and can therefore be executed within only asubset of the different types of computer systems on which the operatingsystems are designed to run. Often, even when an application program orother computational system is ported to additional operating systems,the application program or other computational system can nonethelessrun more efficiently on the operating systems for which the applicationprogram or other computational system was originally targeted. Anotherdifficulty arises from the increasingly distributed nature of computersystems. Although distributed operating systems are the subject ofconsiderable research and development efforts, many of the popularoperating systems are designed primarily for execution on a singlecomputer system. In many cases, it is difficult to move applicationprograms, in real time, between the different computer systems of adistributed computer system for high-availability, fault-tolerance, andload-balancing purposes. The problems are even greater in heterogeneousdistributed computer systems which include different types of hardwareand devices running different types of operating systems. Operatingsystems continue to evolve, as a result of which certain olderapplication programs and other computational entities may beincompatible with more recent versions of operating systems for whichthey are targeted, creating compatibility issues that are particularlydifficult to manage in large distributed systems.

For all of these reasons, a higher level of abstraction, referred to asthe “virtual machine,” (“VM”) has been developed and evolved to furtherabstract computer hardware in order to address many difficulties andchallenges associated with traditional computing systems, including thecompatibility issues discussed above. FIGS. 5A-B show two types of VMand virtual-machine execution environments. FIGS. 5A-B use the sameillustration conventions as used in FIG. 4. FIG. 5A shows a first typeof virtualization. The computer system 500 in FIG. 5A includes the samehardware layer 502 as the hardware layer 402 shown in FIG. 4. However,rather than providing an operating system layer directly above thehardware layer, as in FIG. 4, the virtualized computing environmentshown in FIG. 5A features a virtualization layer 504 that interfacesthrough a virtualization-layer/hardware-layer interface 506, equivalentto interface 416 in FIG. 4, to the hardware. The virtualization layer504 provides a hardware-like interface to VMs, such as VM 510, in avirtual-machine layer 511 executing above the virtualization layer 504.Each VM includes one or more application programs or other higher-levelcomputational entities packaged together with an operating system,referred to as a “guest operating system,” such as application 514 andguest operating system 516 packaged together within VM 510. Each VM isthus equivalent to the operating-system layer 404 andapplication-program layer 406 in the general-purpose computer systemshown in FIG. 4. Each guest operating system within a VM interfaces tothe virtualization layer interface 504 rather than to the actualhardware interface 506. The virtualization layer 504 partitions hardwaredevices into abstract virtual-hardware layers to which each guestoperating system within a VM interfaces. The guest operating systemswithin the VMs, in general, are unaware of the virtualization layer andoperate as if they were directly accessing a true hardware interface.The virtualization layer 504 ensures that each of the VMs currentlyexecuting within the virtual environment receive a fair allocation ofunderlying hardware devices and that all VMs receive sufficient devicesto progress in execution. The virtualization layer 504 may differ fordifferent guest operating systems. For example, the virtualization layeris generally able to provide virtual hardware interfaces for a varietyof different types of computer hardware. This allows, as one example, aVM that includes a guest operating system designed for a particularcomputer architecture to run on hardware of a different architecture.The number of VMs need not be equal to the number of physical processorsor even a multiple of the number of processors.

The virtualization layer 504 includes a virtual-machine-monitor module518 (“VMM”) that virtualizes physical processors in the hardware layerto create virtual processors on which each of the VMs executes. Forexecution efficiency, the virtualization layer attempts to allow VMs todirectly execute non-privileged instructions and to directly accessnon-privileged registers and memory. However, when the guest operatingsystem within a VM accesses virtual privileged instructions, virtualprivileged registers, and virtual privileged memory through thevirtualization layer 504, the accesses result in execution ofvirtualization-layer code to simulate or emulate the privileged devices.The virtualization layer additionally includes a kernel module 520 thatmanages memory, communications, and data-storage machine devices onbehalf of executing VMs (“VM kernel”). The VM kernel, for example,maintains shadow page tables on each VM so that hardware-levelvirtual-memory facilities can be used to process memory accesses. The VMkernel additionally includes routines that implement virtualcommunications and data-storage devices as well as device drivers thatdirectly control the operation of underlying hardware communications anddata-storage devices. Similarly, the VM kernel virtualizes various othertypes of I/O devices, including keyboards, optical-disk drives, andother such devices. The virtualization layer 504 essentially schedulesexecution of VMs much like an operating system schedules execution ofapplication programs, so that the VMs each execute within a complete andfully functional virtual hardware layer.

FIG. 5B shows a second type of virtualization. In FIG. 5B, the computersystem 540 includes the same hardware layer 542 and operating systemlayer 544 as the hardware layer 402 and the operating system layer 404shown in FIG. 4. Several application programs 546 and 548 are shownrunning in the execution environment provided by the operating system544. In addition, a virtualization layer 550 is also provided, incomputer 540, but, unlike the virtualization layer 504 discussed withreference to FIG. 5A, virtualization layer 550 is layered above theoperating system 544, referred to as the “host OS,” and uses theoperating system interface to access operating-system-providedfunctionality as well as the hardware. The virtualization layer 550comprises primarily a VMM and a hardware-like interface 552, similar tohardware-like interface 508 in FIG. 5A. The hardware-layer interface552, equivalent to interface 416 in FIG. 4, provides an executionenvironment for a number of VMs 556-558, each including one or moreapplication programs or other higher-level computational entitiespackaged together with a guest operating system.

In FIGS. 5A-5B, the layers are somewhat simplified for clarity ofillustration. For example, portions of the virtualization layer 550 mayreside within the host-operating-system kernel, such as a specializeddriver incorporated into the host operating system to facilitatehardware access by the virtualization layer.

It should be noted that virtual hardware layers, virtualization layers,and guest operating systems are all physical entities that areimplemented by computer instructions stored in physical data-storagedevices, including electronic memories, mass-storage devices, opticaldisks, magnetic disks, and other such devices. The term “virtual” doesnot, in any way, imply that virtual hardware layers, virtualizationlayers, and guest operating systems are abstract or intangible. Virtualhardware layers, virtualization layers, and guest operating systemsexecute on physical processors of physical computer systems and controloperation of the physical computer systems, including operations thatalter the physical states of physical devices, including electronicmemories and mass-storage devices. They are as physical and tangible asany other component of a computer since, such as power supplies,controllers, processors, busses, and data-storage devices.

A VM or virtual application, described below, is encapsulated within adata package for transmission, distribution, and loading into avirtual-execution environment. One public standard for virtual-machineencapsulation is referred to as the “open virtualization format”(“OVF”). The OVF standard specifies a format for digitally encoding a VMwithin one or more data files. FIG. 6 shows an OVF package. An OVFpackage 602 includes an OVF descriptor 604, an OVF manifest 606, an OVFcertificate 608, one or more disk-image files 610-611, and one or moredevice files 612-614. The OVF package can be encoded and stored as asingle file or as a set of files. The OVF descriptor 604 is an XMLdocument 620 that includes a hierarchical set of elements, eachdemarcated by a beginning tag and an ending tag. The outermost, orhighest-level, element is the envelope element, demarcated by tags 622and 623. The next-level element includes a reference element 626 thatincludes references to all files that are part of the OVF package, adisk section 628 that contains meta information about all of the virtualdisks included in the OVF package, a network section 630 that includesmeta information about all of the logical networks included in the OVFpackage, and a collection of virtual-machine configurations 632 whichfurther includes hardware descriptions of each VM 634. There are manyadditional hierarchical levels and elements within a typical OVFdescriptor. The OVF descriptor is thus a self-describing, XML file thatdescribes the contents of an OVF package. The OVF manifest 606 is a listof cryptographic-hash-function-generated digests 636 of the entire OVFpackage and of the various components of the OVF package. The OVFcertificate 608 is an authentication certificate 640 that includes adigest of the manifest and that is cryptographically signed. Disk imagefiles, such as disk image file 610, are digital encodings of thecontents of virtual disks and device files 612 are digitally encodedcontent, such as operating-system images. A VM or a collection of VMsencapsulated together within a virtual application can thus be digitallyencoded as one or more files within an OVF package that can betransmitted, distributed, and loaded using well-known tools fortransmitting, distributing, and loading files. A virtual appliance is asoftware service that is delivered as a complete software stackinstalled within one or more VMs that is encoded within an OVF package.

The advent of VMs and virtual environments has alleviated many of thedifficulties and challenges associated with traditional general-purposecomputing. Machine and operating-system dependencies can besignificantly reduced or eliminated by packaging applications andoperating systems together as VMs and virtual appliances that executewithin virtual environments provided by virtualization layers running onmany different types of computer hardware. A next level of abstraction,referred to as virtual data centers or virtual infrastructure, provide adata-center interface to virtual data centers computationallyconstructed within physical data centers.

FIG. 7 shows virtual data centers provided as an abstraction ofunderlying physical-data-center hardware components. In FIG. 7, aphysical data center 702 is shown below a virtual-interface plane 704.The physical data center consists of a virtual-data-center managementserver computer 706 and any of various different computers, such as PC708, on which a virtual-data-center management interface may bedisplayed to system administrators and other users. The physical datacenter additionally includes generally large numbers of servercomputers, such as server computer 710, that are coupled together bylocal area networks, such as local area network 712 that directlyinterconnects server computer 710 and 714-720 and a mass-storage array722. The physical data center shown in FIG. 7 includes three local areanetworks 712, 724, and 726 that each directly interconnects a bank ofeight server computers and a mass-storage array. The individual servercomputers, such as server computer 710, each includes a virtualizationlayer and runs multiple VMs. Different physical data centers may includemany different types of computers, networks, data-storage systems anddevices connected according to many different types of connectiontopologies. The virtual-interface plane 704, a logical abstraction layershown by a plane in FIG. 7, abstracts the physical data center to avirtual data center comprising one or more device pools, such as devicepools 730-732, one or more virtual data stores, such as virtual datastores 734-736, and one or more virtual networks. In certainimplementations, the device pools abstract banks of server computersdirectly interconnected by a local area network.

The virtual-data-center management interface allows provisioning andlaunching of VMs with respect to device pools, virtual data stores, andvirtual networks, so that virtual-data-center administrators need not beconcerned with the identities of physical-data-center components used toexecute particular VMs. Furthermore, the virtual-data-center managementserver computer 706 includes functionality to migrate running VMs fromone server computer to another in order to optimally or near optimallymanage device allocation, provides fault tolerance, and highavailability by migrating VMs to most effectively utilize underlyingphysical hardware devices, to replace VMs disabled by physical hardwareproblems and failures, and to ensure that multiple VMs supporting ahigh-availability virtual appliance are executing on multiple physicalcomputer systems so that the services provided by the virtual applianceare continuously accessible, even when one of the multiple virtualappliances becomes compute bound, data-access bound, suspends execution,or fails. Thus, the virtual data center layer of abstraction provides avirtual-data-center abstraction of physical data centers to simplifyprovisioning, launching, and maintenance of VMs and virtual appliancesas well as to provide high-level, distributed functionalities thatinvolve pooling the devices of individual server computers and migratingVMs among server computers to achieve load balancing, fault tolerance,and high availability.

FIG. 8 shows virtual-machine components of a virtual-data-centermanagement server computer and physical server computers of a physicaldata center above which a virtual-data-center interface is provided bythe virtual-data-center management server computer. Thevirtual-data-center management server computer 802 and avirtual-data-center database 804 comprise the physical components of themanagement component of the virtual data center. The virtual-data-centermanagement server computer 802 includes a hardware layer 806 andvirtualization layer 808 and runs a virtual-data-centermanagement-server VM 810 above the virtualization layer. Although shownas a single server computer in FIG. 8, the virtual-data-centermanagement server computer (“VDC management server”) may include two ormore physical server computers that support multipleVDC-management-server virtual appliances. The virtual-data-centermanagement-server VM 810 includes a management-interface component 812,distributed services 814, core services 816, and a host-managementinterface 818. The host-management interface 818 is accessed from any ofvarious computers, such as the PC 708 shown in FIG. 7. Thehost-management interface 818 allows the virtual-data-centeradministrator to configure a virtual data center, provision VMs, collectstatistics and view log files for the virtual data center, and to carryout other, similar management tasks. The host-management interface 818interfaces to virtual-data-center agents 824, 825, and 826 that executeas VMs within each of the server computers of the physical data centerthat is abstracted to a virtual data center by the VDC management servercomputer.

The distributed services 814 include a distributed-device scheduler thatassigns VMs to execute within particular physical server computers andthat migrates VMs in order to most effectively make use of computationalbandwidths, data-storage capacities, and network capacities of thephysical data center. The distributed services 814 further include ahigh-availability service that replicates and migrates VMs in order toensure that VMs continue to execute despite problems and failuresexperienced by physical hardware components. The distributed services814 also include a live-virtual-machine migration service thattemporarily halts execution of a VM, encapsulates the VM in an OVFpackage, transmits the OVF package to a different physical servercomputer, and restarts the VM on the different physical server computerfrom a virtual-machine state recorded when execution of the VM washalted. The distributed services 814 also include a distributed backupservice that provides centralized virtual-machine backup and restore.

The core services 816 provided by the VDC management server VM 810include host configuration, virtual-machine configuration,virtual-machine provisioning, generation of virtual-data-center alertsand events, ongoing event logging and statistics collection, a taskscheduler, and a device-management module. Each physical servercomputers 820-822 also includes a host-agent VM 828-830 through whichthe virtualization layer can be accessed via a virtual-infrastructureapplication programming interface (“API”). This interface allows aremote administrator or user to manage an individual server computerthrough the infrastructure API. The virtual-data-center agents 824-826access virtualization-layer server information through the host agents.The virtual-data-center agents are primarily responsible for offloadingcertain of the virtual-data-center management-server functions specificto a particular physical server to that physical server computer. Thevirtual-data-center agents relay and enforce device allocations made bythe VDC management server VM 810, relay virtual-machine provisioning andconfiguration-change commands to host agents, monitor and collectperformance statistics, alerts, and events communicated to thevirtual-data-center agents by the local host agents through theinterface API, and to carry out other, similar virtual-data-managementtasks.

The virtual-data-center abstraction provides a convenient and efficientlevel of abstraction for exposing the computational devices of acloud-computing facility to cloud-computing-infrastructure users. Acloud-director management server exposes virtual devices of acloud-computing facility to cloud-computing-infrastructure users. Inaddition, the cloud director introduces a multi-tenancy layer ofabstraction, which partitions VDCs into tenant-associated VDCs that caneach be allocated to an individual tenant or tenant organization, bothreferred to as a “tenant.” A given tenant can be provided one or moretenant-associated VDCs by a cloud director managing the multi-tenancylayer of abstraction within a cloud-computing facility. The cloudservices interface (308 in FIG. 3) exposes a virtual-data-centermanagement interface that abstracts the physical data center.

FIG. 9 shows a cloud-director level of abstraction. In FIG. 9, threedifferent physical data centers 902-904 are shown below planesrepresenting the cloud-director layer of abstraction 906-908. Above theplanes representing the cloud-director level of abstraction,multi-tenant virtual data centers 910-912 are shown. The devices ofthese multi-tenant virtual data centers are securely partitioned inorder to provide secure virtual data centers to multiple tenants, orcloud-services-accessing organizations. For example, acloud-services-provider virtual data center 910 is partitioned into fourdifferent tenant-associated virtual-data centers within a multi-tenantvirtual data center for four different tenants 916-919. Eachmulti-tenant virtual data center is managed by a cloud directorcomprising one or more cloud-director server computers 920-922 andassociated cloud-director databases 924-926. Each cloud-director servercomputer or server computers runs a cloud-director virtual appliance 930that includes a cloud-director management interface 932, a set ofcloud-director services 934, and a virtual-data-center management-serverinterface 936. The cloud-director services include an interface andtools for provisioning multi-tenant virtual data center virtual datacenters on behalf of tenants, tools and interfaces for configuring andmanaging tenant organizations, tools and services for organization ofvirtual data centers and tenant-associated virtual data centers withinthe multi-tenant virtual data center, services associated with templateand media catalogs, and provisioning of virtualization networks from anetwork pool. Templates are VMs that each contains an OS and/or one ormore VMs containing applications. A template may include much of thedetailed contents of VMs and virtual appliances that are encoded withinOVF packages, so that the task of configuring a VM or virtual applianceis significantly simplified, requiring only deployment of one OVFpackage. These templates are stored in catalogs within a tenant'svirtual-data center. These catalogs are used for developing and stagingnew virtual appliances and published catalogs are used for sharingtemplates in virtual appliances across organizations. Catalogs mayinclude OS images and other information relevant to construction,distribution, and provisioning of virtual appliances.

Considering FIGS. 7 and 9, the VDC-server and cloud-director layers ofabstraction can be seen, as discussed above, to facilitate employment ofthe virtual-data-center concept within private and public clouds.However, this level of abstraction does not fully facilitate aggregationof single-tenant and multi-tenant virtual data centers intoheterogeneous or homogeneous aggregations of cloud-computing facilities.

FIG. 10 shows virtual-cloud-connector nodes (“VCC nodes”) and a VCCserver, components of a distributed system that provides multi-cloudaggregation and that includes a cloud-connector server andcloud-connector nodes that cooperate to provide services that aredistributed across multiple clouds. VMware vCloud™ VCC servers and nodesare one example of VCC server and nodes. In FIG. 10, seven differentcloud-computing facilities are shown 1002-1008. Cloud-computing facility1002 is a private multi-tenant cloud with a cloud director 1010 thatinterfaces to a VDC management server 1012 to provide a multi-tenantprivate cloud comprising multiple tenant-associated virtual datacenters. The remaining cloud-computing facilities 1003-1008 may beeither public or private cloud-computing facilities and may besingle-tenant virtual data centers, such as virtual data centers 1003and 1006, multi-tenant virtual data centers, such as multi-tenantvirtual data centers 1004 and 1007-1008, or any of various differentkinds of third-party cloud-services facilities, such as third-partycloud-services facility 1005. An additional component, the VCC server1014, acting as a controller is included in the private cloud-computingfacility 1002 and interfaces to a VCC node 1016 that runs as a virtualappliance within the cloud director 1010. A VCC server may also run as avirtual appliance within a VDC management server that manages asingle-tenant private cloud. The VCC server 1014 additionallyinterfaces, through the Internet, to VCC node virtual appliancesexecuting within remote VDC management servers, remote cloud directors,or within the third-party cloud services 1018-1023. The VCC serverprovides a VCC server interface that can be displayed on a local orremote terminal, PC, or other computer system 1026 to allow acloud-aggregation administrator or other user to accessVCC-server-provided aggregate-cloud distributed services. In general,the cloud-computing facilities that together form amultiple-cloud-computing aggregation through distributed servicesprovided by the VCC server and VCC nodes are geographically andoperationally distinct.

As mentioned above, while the virtual-machine-based virtualizationlayers, described in the previous subsection, have received widespreadadoption and use in a variety of different environments, from personalcomputers to enormous distributed computing systems, traditionalvirtualization technologies are associated with computational overheads.While these computational overheads have steadily decreased, over theyears, and often represent ten percent or less of the totalcomputational bandwidth consumed by an application running above a guestoperating system in a virtualized environment, traditionalvirtualization technologies nonetheless involve computational costs inreturn for the power and flexibility that they provide.

While a traditional virtualization layer can simulate the hardwareinterface expected by any of many different operating systems, OSLvirtualization essentially provides a secure partition of the executionenvironment provided by a particular operating system. As one example,OSL virtualization provides a file system to each container, but thefile system provided to the container is essentially a view of apartition of the general file system provided by the underlyingoperating system of the host. In essence, OSL virtualization usesoperating-system features, such as namespace isolation, to isolate eachcontainer from the other containers running on the same host. In otherwords, namespace isolation ensures that each application is executedwithin the execution environment provided by a container to be isolatedfrom applications executing within the execution environments providedby the other containers. A container cannot access files that are notincluded in the container's namespace and cannot interact withapplications running in other containers. As a result, a container canbe booted up much faster than a VM, because the container usesoperating-system-kernel features that are already available andfunctioning within the host. Furthermore, the containers sharecomputational bandwidth, memory, network bandwidth, and othercomputational resources provided by the operating system, without theoverhead associated with computational resources allocated to VMs andvirtualization layers. Again, however, OSL virtualization does notprovide many desirable features of traditional virtualization. Asmentioned above, OSL virtualization does not provide a way to rundifferent types of operating systems for different groups of containerswithin the same host and OSL-virtualization does not provide for livemigration of containers between hosts, high-availability functionality,distributed resource scheduling, and other computational functionalityprovided by traditional virtualization technologies.

FIG. 11 shows an example server computer used to host three containers.As discussed above with reference to FIG. 4, an operating system layer404 runs above the hardware 402 of the host computer. The operatingsystem provides an interface, for higher-level computational entities,that includes a system-call interface 428 and the non-privilegedinstructions, memory addresses, and registers 426 provided by thehardware layer 402. However, unlike in FIG. 4, in which applications rundirectly above the operating system layer 404, OSL virtualizationinvolves an OSL virtualization layer 1102 that provides operating-systeminterfaces 1104-1106 to each of the containers 1108-1110. Thecontainers, in turn, provide an execution environment for an applicationthat runs within the execution environment provided by container 1108.The container can be thought of as a partition of the resourcesgenerally available to higher-level computational entities through theoperating system interface 430.

FIG. 12 shows an approach to implementing the containers on a VM. FIG.12 shows a host computer similar to that shown in FIG. 5A, discussedabove. The host computer includes a hardware layer 502 and avirtualization layer 504 that provides a virtual hardware interface 508to a guest operating system 1102. Unlike in FIG. 5A, the guest operatingsystem interfaces to an OSL-virtualization layer 1104 that providescontainer execution environments 1206-1208 to multiple applicationprograms.

Note that, although only a single guest operating system and OSLvirtualization layer are shown in FIG. 12, a single virtualized hostsystem can run multiple different guest operating systems withinmultiple VMs, each of which supports one or more OSL-virtualizationcontainers. A virtualized, distributed computing system that uses guestoperating systems running within VMs to support OSL-virtualizationlayers to provide containers for running applications is referred to, inthe following discussion, as a “hybrid virtualized distributed computingsystem.”

Running containers above a guest operating system within a VM providesadvantages of traditional virtualization in addition to the advantagesof OSL virtualization. Containers can be quickly booted in order toprovide additional execution environments and associated resources foradditional application instances. The resources available to the guestoperating system are efficiently partitioned among the containersprovided by the OSL-virtualization layer 1204 in FIG. 12, because thereis almost no additional computational overhead associated withcontainer-based partitioning of computational resources. However, manyof the powerful and flexible features of the traditional virtualizationtechnology can be applied to VMs in which containers run above guestoperating systems, including live migration from one host to another,various types of high-availability and distributed resource scheduling,and other such features. Containers provide share-based allocation ofcomputational resources to groups of applications with guaranteedisolation of applications in one container from applications in theremaining containers executing above a guest operating system. Moreover,resource allocation can be modified at run time between containers. Thetraditional virtualization layer provides for flexible and scaling overlarge numbers of hosts within large distributed computing systems and asimple approach to operating-system upgrades and patches. Thus, the useof OSL virtualization above traditional virtualization in a hybridvirtualized distributed computing system, as shown in FIG. 12, providesmany of the advantages of both a traditional virtualization layer andthe advantages of OSL virtualization.

Methods and Systems for Troubleshooting Anomalous Behavior in a DataCenter Based on Pair-Wise Streams of Metric Data

FIG. 13 shows an example of a virtualization layer 1302 located above aphysical data center 1304. For the sake of illustration, thevirtualization layer 1302 is separated from the physical data center1304 by a virtual-interface plane 1306. The physical data center 1304 isan example of a distributed computing system. The physical data center1304 comprises physical objects, including an administration computersystem 1308, any of various computers, such as PC 1310, on which avirtual-data-center (“VDC”) management interface may be displayed tosystem administrators and other users, server computers, such as servercomputers 1312-1319, data-storage devices, and network devices. Eachserver computer may have multiple network interface cards (“NICs”) toprovide high bandwidth and networking to other server computers and datastorage devices. The server computers may be networked together to formserver-computer groups within the data center 1304. The example physicaldata center 1304 includes three server-computer groups each of whichhave eight server computers. For example, server-computer group 1320comprises interconnected server computers 1312-1319 that are connectedto a mass-storage array 1322. Within each server-computer group, certainserver computers are grouped together to form a cluster that provides anaggregate set of resources (i.e., resource pool) to objects in thevirtualization layer 1302. Different physical data centers may includemany different types of computers, networks, data-storage systems anddevices connected according to many different types of connectiontopologies.

The virtualization layer 1302 includes virtual objects, such as VMs,applications, and containers, hosted by the server computers in thephysical data center 1304. The virtualization layer 1302 may alsoinclude a virtual network (not illustrated) of virtual switches,routers, load balancers, and NICs formed from the physical switches,routers, and NICs of the physical data center 1304. Certain servercomputers host VMs and containers as described above. For example,server computer 1318 hosts two containers identified as Cont₁ and Cont₂;cluster of server computers 1312-1314 host six VMs identified as VM₁,VM₂, VM₃, VM₄, VM₅, and VM₆; server computer 1324 hosts four VMsidentified as VM₇, VM₈, VM₉, VM₁₀. Other server computers may hostapplications as described above with reference to FIG. 4. For example,server computer 1326 hosts an application identified as App₄.

The virtual-interface plane 1306 abstracts the resources of the physicaldata center 1304 to one or more VDCs comprising the virtual objects andone or more virtual data stores, such as virtual data stores 1328 and1330. For example, one VDC may comprise the VMs running on servercomputer 1324 and virtual data store 1328. Automated methods and systemsdescribed herein may be executed by an operations manager 1332 in one ormore VMs on the administration computer system 1308. The operationsmanager 1332 provides several interfaces, such as graphical userinterfaces, for data center management, system administrators, andapplication owners. The operations manager 1332 receives streams ofmetric data from various physical and virtual objects of the data centeras described below.

In the following discussion, the term “object” refers to a physicalobject, such as a server computer and a network device, or to a virtualobject, such as an application, VM, virtual network device, container,or any other physical or virtual object of the physical data center 1304for which metric data can be collected to evaluate abnormal or normalbehavior of the object. The term “resource” refers to a physicalresource of the data center, such as, but are not limited to, aprocessor, a core, memory, a network connection, network interface,data-storage device, a mass-storage device, a switch, a router, andother any other component of the physical data center 1304. Resources ofa server computer and clusters of server computers may form a resourcepool for creating virtual resources of a virtual infrastructure used torun virtual objects. The term “resource” may also refer to a virtualresource, which may have been formed from physical resources assigned toa virtual object. For example, a resource may be a virtual processorused by a virtual object formed from one or more cores of a multicoreprocessor, virtual memory formed from a portion of physical memory and ahard drive, virtual storage formed from a sector or image of a hard diskdrive, a virtual switch, and a virtual router. Each virtual object usesonly the physical resources assigned to the virtual object.

The operations manager 1332 monitors physical and virtual resources foranomalous behavior by collecting numerous streams of time-dependentmetric data. Each stream of metric data is time series data that may begenerated by an operating system, a resource, or by an object itself. Astream of metric data associated with a resource comprises a sequence oftime-ordered metric values that are recorded in spaced points in timecalled “time stamps.” A stream of metric data is simply called a“metric” and is denoted byv(t)=(x _(i))_(i=1) ^(N)=(x(t _(i)))_(i=1) ^(N)  (1)

where

-   -   v denotes the name of the metric;    -   N is the number of metric values in the sequence;    -   x_(i)=x(t_(i)) is a metric value;    -   t_(i) is a time stamp indicating when the metric value was        recorded in a data-storage device; and    -   subscript i is a time stamp index i=1, . . . , N.

FIG. 14A shows a plot of an example metric. Horizontal axis 1402represents time. Vertical axis 1404 represents a range of metric valueamplitudes. Curve 1406 represents a metric as time series data. Inpractice, a metric comprises a sequence of discrete metric values inwhich each metric value is recorded in a data-storage device. FIG. 14Aincludes a magnified view 1408 of three consecutive metric valuesrepresented by points. Each point represents an amplitude of the metricat a corresponding time stamp. For example, points 1410-1412 representconsecutive metric values (i.e., amplitudes) x_(i−1), x_(i), and x_(i+1)recorded in a data-storage device at corresponding time stamps t_(i−1),t_(i), and t_(i+1). The example metric may represent usage of a physicalor virtual resource. For example, the metric may represent CPU usage ofa core in a multicore processor of a server computer over time. Themetric may represent the amount of virtual memory a VM uses over time.The metric may represent network throughput for a server computer.Network throughput is the number of bits of data transmitted to and froma physical or virtual object and is recorded in megabits, kilobits, orbits per second. The metric may represent network traffic for a servercomputer. Network traffic at a physical or virtual object is a count ofthe number of data packets received and sent per unit of time. Themetric may also represent object performance, such as CPU contention,response time to requests, and wait time for access to a resource of anobject.

Each object may have numerous associated metrics. A server computer mayhave hundreds of associated metrics including usage of each core of amulticore core processor, memory usage, storage usage, networkthroughput, error rates, datastores, disk usage, average response times,peak response times, thread counts, and power usage just to name a few.A virtual object, such as a VM, may have hundreds of associated metricsthat monitor both physical and virtual resource usage, such as virtualCPU metrics, virtual memory usage metrics, virtual disk usage, virtualstorage space, number of data stores, average and peak response timesfor various physical and virtual resources of the VM, networkthroughput, and power usage just to name a few.

In FIGS. 14B-14C, the operations manager 1332 receives numerous metricsassociated with numerous resources and objects. Directional arrowsrepresent metrics sent from physical and virtual resources to theoperations manager 1332. In FIG. 14B, the operating systems of PC 1310,server computers 1308 and 1324, and mass-storage array 1322 send metricsto the operations manager 1332. A cluster of server computers 1312-1314send metrics to the operations manager 1332. In FIG. 14C, the operatingsystems, VMs, containers, applications, and virtual storage mayindependently send metrics to the operations manager 1332. Certainobjects may send metrics as the time series data is generated whileother objects may only send time series data of a metric at certaintimes or when requested to send metrics by the operations manager 1332.The operations manager 1332 may be implemented in a VM to collect andprocesses the metrics to identify abnormal behaving objects and maygenerate recommendations to correct abnormally behaving objects orexecute remedial measures, such as reconfiguring a virtual network of aVDC or migrating VMs from one server computer to another. For example,remedial measures may include, but are not limited to, powering downserver computers, replacing VMs disabled by physical hardware problemsand failures, spinning up cloned VMs on additional server computers toensure that the services provided by the VMs are accessible toincreasing demand or when one of the VMs becomes compute or data-accessbound.

Unexpected abnormal behavior in an object of a data center may be theresult of abnormal behavior exhibited by another object at differentlevels of an object topology of a data center. Alternatively, abnormalbehavior in an object of a data center may create unexpected abnormalbehavior exhibited by other objects located in different levels of theobject topology. These unexpected abnormal behaviors may be exhibited incorrelations of the associated metrics for specific time intervals.

An object topology of objects of a data center is determined byparent/child relationships between the objects comprising the set. Forexample, a server computer is a parent with respect VMs (i.e., children)executing on the host, and, at the same time, the server computer is achild with respect to a cluster (i.e., parent). The object topology maybe represented as a graph of objects. The object topology for a set ofobjects may be dynamically created by the operations manager 1332subject to continuous updates to VMs and server computers and otherchanges to the data center.

FIG. 15A shows an example object topology of a cluster in a data center.In this example, a cluster 1502 comprises four server computers,identified as SC₁, SC₂, SC₃, and SC₄, that are networked together toprovide computational and network resources for virtual objects in avirtualization level 1504. The physical resources of the cluster 1502are aggregated to create virtual resources for the virtual objects inthe virtualization layer 1504. The virtual objects include six VMs1506-1511, three virtual switches 1512-1514, and two datastores1516-1517. In FIG. 15A, an example object topology 1518 comprises fourlevels. A first level of objects comprises the VMs 1506-1511 that sharethe same resources of the cluster 1502. A second level of objectscomprises the virtual switches 1512-1514 of a virtual network thatconnects the VMs 1506-1511 to each other. A third level comprises thedatastores 1516 and 1517 that store the virtual hard disks of the VMs1506-1511. A fourth level comprises the server computers SC₁, SC₂, SC₃,and SC₄. In other implementations, the object topology may be furtherdivided based on whether the objects are related. For example, the firstlevel of VMs may be divided into two levels if VMs 1506-1509 comprisemodules of a first distributed application and VMs 1510 and 1511comprise modules of a second distributed application. An object topologymay include objects of multiple server computers in a data center. FIG.15B shows an example object topology 1520 formed from the objecttopology of objects 1518 in FIG. 15A expanded to include objects of aserver computer identified as SC₅. Virtual objects of the servercomputer SC₅ include four VMs 1522-1525, a virtual switch 1526, and adatastore 1528.

FIG. 15B also shows three example plots 1530-1532 of metrics associatedwith VM 1506, VM 1508, and server computer SC₅. Plot 1530 shows a plotof response time for the application executing in VM 1506. Plot 1531shows a plot of virtual memory of the VM 1508. Plot 1532 shows a plot ofCPU contention for cores of a multicore processor of the server computerSC₅. The plots 1530-1532 reveal that before time, t, response time ofthe application executing in the VM 1506, virtual memory of the VM 1508,and CPU contention at the server computer SC₅ are uncorrelated. Aftertime t the response time of the application executing in the VM 1506,virtual memory of the VM 1509, and CPU contention at the server computerSC₅ appear correlated with peaks and troughs occurring at about the sametimes.

Methods and systems described herein compute correlations betweenmetrics of an object exhibiting unexpected abnormal behavior and othermetrics of objects in different levels of an object topology of a datacenter. The object topology contains the object exhibiting unexpectedabnormal behavior. The combination of correlations may be used toidentify a type of problem in the data center. For example, in FIG. 15B,suppose VMs 1506 and 1508 use services provided by the VMs 1522-1525executing on the server computer SC₅. When the services provided by theVMs 1522-1525 are interrupted or perturbed, VM 1506 experiences aresponse time delay revealed by plot 1530 and VM 1508 experiences spikesin memory usage revealed by plot 1531. A system administrator or ownerof the VMs 1506-1511 may be alerted to the abnormally high responsetimes and memory spikes occurring after time t. Methods and systemsdescribed below may be used to determine a correlation between theresponse time and the virtual memory, a correlation between the virtualmemory and the CPU contention, and a correlation between the virtualmemory and the response time. The system administrator or owner maydetermine that the correlation between the virtual memory and theresponse time is a less significant indicator of the problem than thecorrelation between the response time and the CPU contention and thecorrelation between the virtual memory and the CPU contention. Methodsand systems described below enable a system administrator or applicationowner to associate an alert with the combination of correlations betweenthe response time and virtual memory with the CPU contention such thatwhen the same alert is triggered in the future with the same combinationof correlations, the administrator or application owner may immediatelyrecognize the problem and immediately execute appropriate remedialmeasures to correct the problem. For example, the administrator or ownermay have determined that the problem with CPU contention leads todelayed response times and memory spikes at VMs 1506 and 1508,respectively, and is resolved by increasing virtual CPU usage for the VM1522 at the server computer SC₅. Methods and systems may automaticallyexecute a script program that increases virtual CPU usage for the VM1522 when the alert with the same combination of correlations isdetected.

Methods and systems compute correlations between a selected metric,v_(s)(t), of an object in an object topology and metrics, v_(m)(t), ofother objects in the object topology, where v_(s) denotes the name ofthe selected metric, v_(m) denotes the name of the metric associatedwith objects of the object topology, where index m=1, . . . , M, and Mis the number of metrics in the object topology, excluding the selectedmetric. The metric values of the selected metric and the metric valuesof the other metrics of the object topology may have been generated withdifferent intervals between time stamps, the intervals may not beuniform, and the time stamps of the metric values may not be aligned intime. For example, metric data associated with different resources of anobject may be generated periodically at regular intervals and the timestamps of the metric values may be aligned in time. On the other hand,metrics of other resources may be generated nonperiodically and the timestamps of the metric values are not aligned in time. In certain cases,the operations manager 1332 may request metric data from data sources atregular intervals, while in other cases, the metrics may be sent to theoperations manager 1332 at periodic intervals or whenever metric databecomes available. As a result, the metric values of the selected metricand the metric values of other metrics of the object topology may not betime aligned.

In order to compute correlations of the selected metric with each metricof an object topology, the selected metric and other metrics may bealigned in time with a general set of uniformly spaced time stamps.Metric values may be aligned in time by computing a running-time averageof metric values in a sliding time window centered at each time stamp ofthe general set of uniformly spaced time stamps. In an alternativeimplementation, the metric values with time stamps in the sliding timewindow may be smoothed by computing a running time median of metricvalues in the sliding time window centered at each time stamp of thegeneral set of uniformly spaced time stamps. Methods and systems mayalso align in time the metrics by deleting time stamps of missing metricvalues or interpolating missing metric data at time stamps of thegeneral set of uniformly spaced time stamps using interpolation, such aslinear, quadratic, or spline interpolation. After the selected metricand the other metrics of the object topology have been time aligned,correlations are computed for the selected metric v_(s) with each metricv_(m).

In one implementation, for each metric v_(m), a correlation between themetric v_(m) and the selected metric v_(s) may be computed using aproduct-moment correlation coefficient (“PMCC”) given by:

$\begin{matrix}{{{\rho_{pm}\left( {v_{s},v_{m}} \right)} = {{\frac{1}{N}{\sum\limits_{i = 1}^{N}{\left( \frac{x_{i,s} - \mu_{s}}{\sigma_{s}} \right)\left( \frac{x_{i,m} - \mu_{m}}{\sigma_{m}} \right)}}}}}{where}{\mu_{s} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}x_{i,s}}}}{\sigma_{s} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {x_{i,s} - \mu_{s}} \right)}}}{\mu_{m} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}x_{i,m}}}}{and}{\sigma_{m} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {x_{i,m} - \mu_{m}} \right)}}}} & (2)\end{matrix}$

In another implementation, for each metric v_(m), a correlation betweenthe metric v_(m) and the selected metric v_(s) may be computed using adistance correlation. The distance correlation is executed by computingan N×N distance matrix for the selected metric v_(s) with matrixelements given bya _(j,k)=√{square root over (x _(j,s) ² −z _(k,s) ²)}  (3a)

where j, k=1, 2, . . . , N.

The matrix elements of Equation (3a) are doubly centered to obtain adoubly centered distance matrix for the selected metric v_(s) withmatrix elements given by

$\begin{matrix}{{A_{j,k} = {a_{j,k} - {\overset{\_}{a}}_{j} - {\overset{\_}{a}}_{k} + \overset{\_}{a}}}{where}{{\overset{\_}{a}}_{j} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}a_{j,k}}}}{{\overset{\_}{a}}_{k} = {\frac{1}{N}{\sum\limits_{j = 1}^{N}a_{j,k}}}}{and}{\overset{\_}{a} = {\frac{1}{2N}\left( {{\sum\limits_{j = 1}^{N}{\overset{\_}{a}}_{j}} + {\sum\limits_{k = 1}^{N}{\overset{\_}{a}}_{k}}} \right)}}} & \left( {3b} \right)\end{matrix}$For each metric v_(m), matrix elements of a corresponding N×N distancematrix are given byb _(j,k)=√{square root over (x _(j,m) ² −x _(k,m) ²)}  (4a)

where j, k=1, 2, . . . , N.

The matrix elements of Equation (4a) are doubly centered to obtain adoubly centered distance matrix for the metric v_(m) with matrixelements given by

$\begin{matrix}{{B_{j,k} = {b_{j,k} - {\overset{\_}{b}}_{j} - {\overset{\_}{b}}_{k} + \overset{\_}{b}}}{where}{{\overset{\_}{b}}_{j} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}b_{j,k}}}}{{\overset{\_}{b}}_{k} = {\frac{1}{N}{\sum\limits_{j = 1}^{N}b_{j,k}}}}{and}{\overset{\_}{b} = {\frac{1}{2N}\left( {{\sum\limits_{j = 1}^{N}{\overset{\_}{b}}_{j}} + {\sum\limits_{k = 1}^{N}{\overset{\_}{b}}_{k}}} \right)}}} & \left( {4b} \right)\end{matrix}$The distance correlation coefficient is given by

$\begin{matrix}{{{\rho_{dc}\left( {v_{s},v_{m}} \right)} = \frac{{d{Cor}}\left( {v_{s},v_{m}} \right)}{\sqrt{{{d{Var}}\left( v_{s} \right)}{{d{Var}}\left( v_{m} \right)}}}}{where}{{{d{Cor}}\left( {v_{s},v_{m}} \right)} = {\frac{1}{N^{2}}{\sum\limits_{j = 1}^{N}{\sum\limits_{k = 1}^{N}{A_{j,k}B_{j,k}}}}}}{{{d{Var}}\left( v_{s} \right)} = {\frac{1}{N^{2}}{\sum\limits_{j = 1}^{N}{\sum\limits_{k = 1}^{N}A_{j,k}^{2}}}}}{and}{{{d{Var}}\left( v_{m} \right)} = {\frac{1}{N^{2}}{\sum\limits_{j = 1}^{N}{\sum\limits_{k = 1}^{N}B_{j,k}^{2}}}}}} & (5)\end{matrix}$

In another implementation, for each metric v_(m), a correlation betweenthe metric v_(m) and the selected metric v_(s) may be computed usingrank correlation. A rank correlation coefficient is computed using theproduct-moment correlation coefficient of Equation (2) with elements ofthe selected metric v_(s) and the metric v_(m) rank ordered:

$\begin{matrix}{{{\rho_{rc}\left( {v_{s},v_{m}} \right)} = {{\rho_{pm}\left( {{rv}_{s},{rv}_{m}} \right)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\left( \frac{{rx}_{i,s} - \mu_{s}}{\sigma_{s}} \right)\left( \frac{{rx}_{i,m} - \mu_{m}}{\sigma_{m}} \right)}}}}}{where}{{rv}_{s} = \left( {rx}_{i,s} \right)_{i = 1}^{N}}{and}{{rv}_{m} = \left( {rx}_{i,m} \right)_{i = 1}^{N}}} & (6)\end{matrix}$Elements of the rank ordered selected metric, rv_(s), and the rankordered metric, rv_(m), may be obtained by rank ordering elements of thecorresponding selected metric v_(s) and the metric v_(m)(t) from largestto smallest (or smallest to largest). For example, rx_(1,s) may beassigned the largest element in v_(s), rx_(2,s) may be assigned thesecond largest element in v_(s) and so on with the final elementrx_(N,s) assigned the smallest element in v_(s). The elements of therank ordered metric rv_(m) are obtained in the same manner.

Correlation coefficients are computed for the selected metric v_(s) andthe other metrics v_(m) of an object topology in a recent time intervalas follows. 1) Correlation coefficients are computed for the selectedmetric and other metrics of the object associated with the selectedmetric. 2) Correlation coefficients are computed for the selected metricand other metrics of objects at the same level of the object topology.3) Correlation coefficients are computed for the selected metric andother metrics of objects at different levels of the object topology. 4)Correlation coefficients may be computed for the selected metric andother metrics outside the object topology as selected by a user, such asby a system administrator or an application owner.

FIG. 16A shows an example object topology comprising three levels1601-1603 of data center objects. Each object is represented by arectangle. The objects may be virtual objects or physical objects. FIG.16A shows a plot 1606 of metric data of a selected metric v_(s)associated with an object, O_(A). The time axis represents a recent timeinterval that begins at time t, where t denotes point in time that maybe selected by a system administrator after which correlations betweenthe selected metric and other metrics of the object topology arecomputed. The selected metric v_(s) may have been selected forinvestigation by a system administrator. Alternatively, the selectedmetric v_(s) may have been identified because recently generated metricvalues violated a threshold for the metric, indicating a performanceproblem with the object O_(A). If the object O_(A) is a physical object,such as a server computer, the selected metric v_(s) may represent CPUusage, memory usage, network throughput, request per second, errorrates, average response time, CPU contention, uptime, and thread countjust to name a few. If the object O_(A) is a virtual object, such as avirtual machine, the selected metric v_(s) may represent virtual CPUusage, virtual memory usage, virtual network throughput, request persecond, error rates, and average response time just to name a few.

FIGS. 16B-16E show example correlation coefficients calculated over therecent time interval for the selected metric and metrics of objects indifferent levels of the object topology shown in FIG. 16A. Objects ofthe object topology are denoted by O_(Y) with associated metrics denotedby v_(m,Y) ^(x), where the subscript Y identifies the object and thesuperscript x is a numerical value that distinguishes different metricsof the object. For example, the metrics v_(m,Y) ¹ and v_(m,Y) ² mayrepresent CPU usage and memory usage of the object O_(Y). A correlationcoefficient, ρ, is computed for the selected metric with each of theother metrics in the object topology. The correlation coefficient ρrepresents the PMCC, the distance correlation, or the rank correlation.

FIG. 16B shows correlation coefficients for the selected metric andother metrics of the object O_(A). The object O_(A) is enlarged torepresent computation of correlations for the selected metric v_(s) withother metrics v_(m,A) ¹, v_(m,A) ², . . . associated with the objectO_(A). FIG. 16B shows a plot 1608 of metric data of the metric v_(m,A) ¹in the recent time interval. Ellipsis 1610 denotes other metrics of theobject O_(A) that are not represented. A correlation coefficient iscomputed for the selected metric with each of the other metrics. Forexample, ρ(v_(s),v_(m,A) ¹) is the correlation coefficient for theselected metric v_(s) and the metric v_(m,A) ¹ and ρ(v_(s), v_(m,A) ²)is the correlation coefficient between of the selected metric v_(s) andthe metric v_(m,A) ².

FIG. 16C shows correlation coefficients for the selected metric andmetrics of objects in the second level. An object O_(B) represents anobject in the second level 1602 and is shown enlarged to representcomputation of correlations for the selected metric v_(s) with metricsv_(m,B) ¹, v_(m,B) ², . . . of the object O_(B). FIG. 16C shows a plot1612 of metric data of the metric v_(m,B) ¹ in the recent time interval.Ellipsis 1614 denotes other metrics of the object O_(B), that are notrepresented. A correlation is computed for the selected metric with eachof the metrics associated with the object O_(B) and for the metrics ofother objects in the second level. For example, ρ(v_(s), v_(m,B) ¹) isthe correlation coefficient between the selected metric v_(s) and themetric v_(m,B) ¹ and ρ(v_(s), v_(m,B) ²) is the correlation coefficientbetween of the selected metric v_(s) and the metric v_(m,B) ².

FIG. 16D shows correlations for the selected metric with metrics ofobjects in the first level. An object O_(C) represents an object in thefirst level 1601 and is shown enlarged to represent computation ofcorrelations for the selected metric v_(s)(t) with metrics v_(m,C) ¹,v_(m,C) ², . . . of the object O_(C). FIG. 16D shows a plot 1616 ofmetric data of the metric v_(m,C) ¹ in the recent time interval.Ellipsis 1618 denotes other metrics of the object O_(C) that are notrepresented. A correlation is computed for the selected metric with eachof the metrics associated with the object O_(C) and for the metrics ofother objects in the first level. For example, ρ(v_(s), v_(m),c) is thecorrelation coefficient between the selected metric v_(s) and the metricv_(m,C) ¹ and ρ(v_(s), v_(m,C) ²) is the correlation coefficient betweenof the selected metric v_(s) and the metric v_(m,C) ².

FIG. 16E shows correlations for the selected metric with metrics ofobjects in the first level. An object O_(D) represents an object in thethird level 1602 and is shown enlarged to represent computation ofcorrelations for the selected metric v_(s) with metrics v_(m,D) ¹,v_(m,D) ², . . . of the object O_(D). FIG. 16E shows a plot 1620 ofmetric data of the metric v_(m,D) ¹ in the recent time interval.Ellipsis 1622 denotes other metrics of the object O_(C) that are notrepresented. A correlation is computed for the selected metric with eachof the metrics associated with the object O_(D) and for the metrics ofother objects in the third level. For example, ρ(v_(s), v_(m,D) ¹) isthe correlation coefficient between the selected metric v_(s) and themetric v_(m,D) ¹ and ρ(v_(s), v_(m,D) ²) is the correlation coefficientbetween of the selected metric v_(s) and the metric v_(m,D) ².

Correlation coefficients of a selected metric and metrics of objects inan object topology computed over a recent time interval may be comparedto a correlation threshold to determined which metrics are correlatedwith the selected metric. A metric v_(m,Y) ^(x)(t) is identified ascorrelated with the selected metric v_(s)(t) when the followingcondition is satisfied:ρ(v _(s) ,v _(m,Y) ^(x))>Th _(cor)  (7)

where

-   -   Th_(cor) is a correlation threshold; and    -   0<Th_(cor)<1 (e.g., Th_(cor)=0.80).        If none of the metrics of objects in the object topology satisfy        the condition given by Equation (7) for one of the correlation        techniques, the process of computing correlation coefficients        for the selected metric with the metrics of the object topology        may be repeated for a different correlation technique. For        example, if none of the correlation coefficients computed using        PMCC satisfy the condition given by Equation (7), computation of        the correlation coefficients for the selected metric with the        metrics of the objects in the object topology may be repeated        using the distance correlation of Equation (5) or the rank        correlation of Equation (6). The metrics and associated        correlation coefficients may be stored in an unexpected metric        file. The correlation coefficients that satisfy the condition in        Equation (7) are used to rank order the corresponding metrics.        The metric with the largest correlation coefficient over the        recent time interval is assigned the highest rank. The metric        with the second largest correlation coefficient over the recent        time interval is assigned the second highest rank and so on.

FIG. 17 shows an example table of rank ordered metrics of objects of anobject topology. Column 1701 list the rank with the number 1corresponding to the highest rank. Column 1702 list correlationcoefficients for the selected metric v_(s) with metrics of objects ofthe object topology that satisfy the condition represented by Equation(7). Column 1703 list the metrics with correlation coefficients incolumn 1702. In this example, the metric v_(m,C) ³ has the highestcorrelation with the selected metric v_(s). The metric v_(m,F) ¹ has thesecond highest correlation with the selected metric v_(s).

The list of rank ordered correlated metrics may be reduced to a rankordered list of unexpected metrics by discarding correlated metrics thatare correlated with the selected metric over the recent time intervaland are correlated with the selected metric over historical timeintervals when the objects in the object topology exhibited normalbehavior. The unexpected metrics are metrics that have not historicallybeen correlated with the selected metric and may be useful introubleshooting a performance problem.

FIG. 18 shows an example of reducing the list of rank ordered correlatedmetrics to a rank order list of unexpected metrics by discarding metricsin the table of FIG. 17 that are correlated with the selected metricover historical time intervals when the objects in the object topologyexhibited normal behavior. FIG. 18 shows an example table of historicalrank ordered metrics of objects in the object topology. Column 1801 listthe historical rank ordered metrics. Column 1802 list the top tencorrelation coefficients for the selected metric with metrics of theobjects of the object topology over historical time intervals when theobjects in the object topology exhibited normal behavior. Column 1803list the metrics that correspond to the correlation coefficients incolumn 1802. Lines 1804-1806 identify three recent rank ordered metricsthat are historically rank ordered metrics. Lines 1808-1810 representdiscarding the metrics that are historically correlated with theselected metric to obtain a list of rank ordered unexpected metricslisted in column 1812.

FIGS. 19A-19C show an example graphical use interface (“GUI”) thatenables a user to select a metric and view correlated metrics of theobject topology described above with reference to FIGS. 16A-16E. In FIG.19A, names of the metrics are listed in a metrics window 1902. In thisexample, a user has selected the selected metric v_(s), described aboveas indicated by shading. The user may have selected the selected metricbecause an alert was previously generated indicating that the selectedmetric violated an associated threshold or an alert may have beengenerated indicating that a problem has occurred with the object O_(A).The user selects objects of an object topology to compute correlationswith the selected metric using a drop-down menu 1904. The objecttopology associated with the selected metric is displayed in window 1906with the object associated with the selected metric identified by acircle 1908. The user then initiates the process of determiningcorrelated metrics of objects in the object topology by clicking on the“Get correlated metrics” button 1910. In field 1912, the user may alsoselect a number of highest ranked unexpected metrics that are correlatedwith the selected metric to view in window 1914. The user may then viewthe selected metric and the highest ranked unexpected metrics that arecorrelated with the selected metric using the scroll bar 1916. FIGS. 19Band 19C show example plots of the two highest ranked unexpected metricsof FIG. 18.

After executing processes for determining unexpected metrics that arecorrelated with the selected metric, a user may rate the unexpectedmetrics that were helpful in troubleshooting the problem associated withthe selected metric. Poorly rated unexpected metrics may be discardedfrom the list of recommended metrics. By contrast, highly ratedunexpected metrics may be used to troubleshoot the performance problemand are saved to troubleshoot and generate recommendations for remedyingthe problem in the future.

FIGS. 20A-20B show a GUI with a user rating window used to assign a userrating to each of the top ranked unexpected metrics. In FIG. 20A, theGUI list the unexpected metrics in a column 2002 and associatedcorrelation coefficients in a column 2004 obtained as described abovewith reference to FIG. 18. Column 2006 list five-star ratings the usermay use to rate each unexpected metric. In this example, a user selectsa rating ranging from zero to five stars, where a zero-star rating foran unexpected metric indicates the user did not find the metric helpfulin troubleshooting the problem with the object O_(A) and an unexpectedmetric with a five-star rating indicates the user found the metric veryhelpful in troubleshooting the performance problem. FIG. 20B shows anexample of user ratings assigned to each of the top ranked unexpectedmetrics. For example, unexpected metrics v_(m,C) ² and v_(m,A) ¹ havebeen assigned five-star ratings indicating the metrics where veryhelpful in troubleshooting the performance problem. By contrast,unexpected metrics v_(m,E) ¹ and v_(m,D) ³ have been assigned zero-starratings indicating these metrics where not help in troubleshooting theperformance problem.

The user may specify a rating cutoff for discarding unexpected metricsregarded as not helpful in troubleshooting a performance problem. Forexample, metrics with user rating below a three-star rating may bediscarded from the list of rank order unexpected metrics. The list ofrank ordered unexpected metrics may be reorganized by discarding metricswith a user rating below the rating cutoff and the unexpected metricswith user ratings above the cutoff may be re-ranked according to theassociated user ratings. FIG. 20C shows unexpected metrics with ratingsbelow three stars discarded as indicated by lines through the metricname and unexpected metrics with user ratings greater than or equal tothree stars rank ordered according to the user ratings.

When the selected metric v_(s) is selected again in the future, thehighest rated unexpected metrics are displayed in a GUI along with anyother metrics with correlation coefficients that satisfy the conditiongiven by Equation (7). The poorly rated unexpected metrics may beexcluded from the list. FIG. 20D shows a GUI that list the highest userrated unexpected metrics obtained as described above with reference toFIGS. 20B-20C and unexpected metrics determined for a most recent timeinterval. The top five entries are the five highest user ratedunexpected metrics determined from a previously executed search forunexpected metrics and list the recently determined unexpected metricswith corresponding correlation coefficients that satisfy the correlationcondition given by Equation (7). The user may also rate the unexpectedmetrics.

Methods and systems may also maintain a record of frequently orperiodically occurring unexpected metrics to immediately identify andremedy performance problems. A system administrator or application ownermay identify a performance problem associated with a set of unexpectedmetrics obtained as described above and determine remedial measures forcorrecting the performance problem. Different frequently or periodicallyoccurring sets of unexpected correlation metrics corresponding todifferent performance problems and remedial measures may be stored in adata storage device.

FIG. 21 shows a table of ten frequently occurring unexpected metricsthat are correlated with the selected metric v_(s). The top row ofcolumns 2101 identify examples of frequently occurring unexpectedmetrics with the selected metric v_(s). For example, entry 2102represents an unexpected metric v_(m,A) ² and the selected metric v_(s)in which the correlation coefficient ρ(v_(m,A) ², v_(s)) has repeatedlysatisfied the condition given by Equation (7). The “X's” in rows ofcolumns 2101 identify combinations of frequently occurring correlationsbetween unexpected metrics and the selected metric. Column 2103 listproblems associated with different combinations of unexpected metricscorrelated with the select metric. For example, Problem (1) may be CPUcontention at a host server computer, Problem (2) may be virtual memoryusage exceeded a threshold, Problem (3) may be average response timeexceeds a limit, and Problem (4) may be the number of thread counts fora server computer has exceeded a threshold. Column 2104 list remedialmeasures that may be taken to correct the corresponding problems listedin column 2103, such as allocating more CPU usage to VMs, increasingvirtual memory or CPU to VMs, and migrating VMs to a different servercomputers. For example, the fourth row indicates that when correlationcoefficients ρ(v_(s), v_(m,A) ¹), ρ(v_(s), v_(m,C) ²), ρ(v_(s), v_(m,C)³) and ρ(v_(s), v_(m,D) ³) satisfy the condition given by Equation (7)and the correlation coefficients ρ(v_(s), v_(m,A) ²), ρ(v_(s), v_(m,B)²), ρ(v_(s), v_(m,B) ⁴), ρ(v_(s), v_(m,D) ²), ρ(v_(s), v_(m,D) ⁴), andρ(v_(s), v_(m,A) ⁵) do not satisfy the condition given by Equation (7),the performance problem is “Problem (4)” and remedial measures “Measure(4)” is recommended to resolve the performance problem.

The methods described below with reference to FIGS. 22-25 are stored inone or more data-storage devices as machine-readable instructions thatwhen executed by one or more processors of the computer system, such asthe computer system shown in FIG. 1, troubleshoot anomalous behavior ina data center.

FIG. 22 is a flow diagram illustrating an example implementation of a“method for troubleshooting anomalous behavior in a data center.” Inblock 2201, a selected metric associated with an object of a data centeris identified. In block 2202, an “identify unexpected metrics of anobject topology of the data center that correspond to a performanceproblem with executing the object” procedure is performed. In block2203, a recommendation to correct the performance problem based on theunexpected metrics is generated.

FIG. 23 is a flow diagram illustrating an example implementation of the“identify unexpected metrics of an object topology of the data centerthat correspond to a performance problem with executing the object”procedure performed in step 2202 of FIG. 22. A loop beginning with block2301 performs the computational operations represented by blocks2302-2311. In block 2302, a “compute correlation coefficients for theselected metric and metrics of objects of the object topology” procedureis performed. A loop beginning with block 2304 performs thecomputational operations represented by blocks 2304-2306 for eachcorrelation coefficient computed in block 2302. In decision block 2304,when a correlation coefficient is greater than a correlation threshold,as described above with reference Equation (7), control flows to 2305.Otherwise, control flows to decision block 2306. In block 2305, themetric and associated correlation coefficient that exceeds thecorrelation threshold are recorded in an unexpected metric file asdescribed above with reference to FIG. 17. In decision block 2306, theoperations represented by blocks 2304 and 2305 are repeated for othercorrelation coefficients. In decision block 2307, when the unexpectedmetric file is empty control flows to block 2311. Otherwise, controlflows to block 2308. In block 2308, an “identify unexpected metricsassociated with the object” procedure is performed. In block 2309, theunexpected metrics in the unexpected metric file are rank ordered asdescribed above with reference to FIGS. 17 and 18 and an associateduser-identified performance problem and remedial measure for correctingthe performance problem are recorded. In block 2310, a “rate theunexpected metrics” procedure is performed. In decision block 2311, whenthe correlation techniques have been exhausted and the unexpected metricfile is empty, control flows to block 2312. In block 2312, a message isdisplayed stating that no metrics of the objects of the object topologyare associated with the selected metric.

FIG. 24 is a flow diagram illustrating an example implementation of the“computing correlation coefficient for the selected metric and metricsof the object associated with the selected metric” procedure performedin step 2302 of FIG. 23. In block 2401, correlation coefficients for theselected metric and metrics of the object associated with the selectedmetric are calculated. In block 2402, correlation coefficients for theselected metric and metrics of the objects at the same level as theobject in the object topology are calculated. In block 2403, correlationcoefficients for the selected metric and metrics of the objects indifferent levels of the object topology are calculated. In decisionblock 2404, when the user has selected objects outside the objecttopology control flows to block 2305. In block 2405, correlationcoefficients for the selected metric and metrics of user objects outsidethe object topology are calculated.

FIG. 25 is a flow diagram illustrating an example implementation of the“identify unexpected metrics associated with the object” procedureperformed in step 2308 of FIG. 23. A loop beginning with block 2501repeats the operations represented by blocks 2502 and 2503 for eachmetric in the unexpected metric file. In decision block 2502, when themetric is a historically correlated metric with the selected metric asdescribed above with reference to FIG. 18, the metric is discarded fromthe unexpected metric file in block 2503. The metrics remaining in theunexpected metric file are unexpected metric that are correlated withthe selected metric. In decision block 2504, blocks 2502 and 2503 arerepeated for another metric in the unexpected metric file.

FIG. 26 is a flow diagram illustrating an example implementation of the“rate the unexpected metrics” procedure performed in step 2310 of FIG.23. A loop beginning with block 2501 repeats the operations representedby blocks 2502-2514 for each metric in the unexpected metric file.Decision blocks 2502-2506 represent a user's decision to rate a metricwith a user rating ranging from zero stars to five stars as describedabove with reference to FIGS. 20A-20B. Blocks 2507-2512 representassigning user rating selected in one of decision blocks 2502-2506 tothe metric. In decision block 2513, when a user rating is below ratingcutoff, the metric is discarded from the unexpected metric file in block2514 as described above with reference to FIG. 20C. In decision block2515, the operations represented by blocks 2502-2514 are repeated foranother metric.

It is appreciated that the previous description of the disclosedembodiments is provided to enable any person skilled in the art to makeor use the present disclosure. Various modifications to theseembodiments will be apparent to those skilled in the art, and thegeneric principles defined herein may be applied to other embodimentswithout departing from the spirit or scope of the disclosure. Thus, thepresent disclosure is not intended to be limited to the embodimentsshown herein but is to be accorded the widest scope consistent with theprinciples and novel features disclosed herein.

The invention claimed is:
 1. A method stored in one or more data-storage devices and executed using one or more processors of a computer system for troubleshooting anomalous behavior in a data center, the method comprising: providing a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem; in response to the user selecting the selected metric, identifying unexpected metrics of an object topology of the data center that are correlated with the selected metric in a recent time interval and are uncorrelated with the selected metric in a historical time interval that precedes the recent time interval and corresponds to when the object did not exhibit the performance problem; and generating a recommendation to correct the performance problem based on the unexpected metrics.
 2. The method of claim 1 wherein identify unexpected metrics of the object topology of the data center comprises: computing correlation coefficients for the selected metric and metrics of objects of the object topology in the recent time interval; discarding metrics of objects of the object topology that are correlated with the selected metric in the historical time interval; and for each correlation coefficient when a correlation coefficient of a metric is greater than a correlation threshold, identifying the metric as an unexpected metric and recording the unexpected metric and associated correlation coefficient in an unexpected metric file, and rank ordering the unexpected metrics in the unexpected metric file.
 3. The method of claim 2 wherein computing correlation coefficients for the selected metric and metrics of objects of the object topology in the recent time interval comprises: calculating correlation coefficients for the selected metric and metrics of the object associated with the selected metric; calculating correlation coefficients for the selected metric and metrics of the objects at the same level as the object in the object topology; calculating correlation coefficients for the selected metric and metrics of the objects in different levels of the object topology; and calculating correlation coefficients for the selected metric and metrics of user identified objects outside the object topology.
 4. The method of claim 1 further comprising: providing a graphical user interface that enables the user to rate each unexpected metric; assigning a user rating to each unexpected metric; discarding unexpected metrics with corresponding user ratings that are less than a user-rating cutoff; rank ordering the unexpected metrics with user ratings that are greater than or equal to the user-rating cutoff; and annotating the unexpected metrics that the user associates with the performance problem.
 5. The method of claim 1 further comprising: determining a frequency of occurrence for a set of unexpected metrics; identifying the performance problem associated with the set of unexpected metrics recording remedial measures for correcting the performance problem; and generating a recommendation to execute the remedial measures for correcting the performance problem when unexpected metrics matches the set of unexpected metrics.
 6. A computer system for troubleshooting anomalous behavior in a data center, the system comprising: one or more processors; one or more data-storage devices; and machine-readable instructions stored in the one or more data-storage devices that when executed using the one or more processors controls the system to perform the operations comprising: providing a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem; in response to the user selecting the selected metric, identifying unexpected metrics of an object topology of the data center that are correlated with the selected metric in a recent time interval and are uncorrelated with the selected metric in a historical time interval that precedes the recent time interval and corresponds to when the object did not exhibit the performance problem; and generating a recommendation to correct the performance problem based on the unexpected metrics.
 7. The system of claim 6 wherein identify unexpected metrics of the object topology of the data center comprises: computing correlation coefficients for the selected metric and metrics of objects of the object topology in the recent time interval; discarding metrics of objects of the object topology that are correlated with the selected metric in the historical time interval; and for each correlation coefficient when a correlation coefficient of a metric is greater than a correlation threshold, identifying the metric as an unexpected metric and recording the unexpected metric and associated correlation coefficient in an unexpected metric file, and rank ordering the unexpected metrics in the unexpected metric file.
 8. The system of claim 7 wherein computing correlation coefficients for the selected metric and metrics of objects of the object topology in the recent time interval comprises: calculating correlation coefficients for the selected metric and metrics of the object associated with the selected metric; calculating correlation coefficients for the selected metric and metrics of the objects at the same level as the object in the object topology; calculating correlation coefficients for the selected metric and metrics of the objects in different levels of the object topology; and calculating correlation coefficients for the selected metric and metrics of user identified objects outside the object topology.
 9. The system of claim 6 further comprising: providing a graphical user interface that enables the user to rate each unexpected metric; assigning a user rating to each unexpected metric; discarding unexpected metrics with corresponding user ratings that are less than a user-rating cutoff; rank ordering the unexpected metrics with user ratings that are greater than or equal to the user-rating cutoff; and annotating the unexpected metrics that user has associated with the performance problem.
 10. The system of claim 6 further comprising: determining a frequency of occurrence for a set of unexpected metrics; identifying the performance problem associated with the set of unexpected metrics recording remedial measures for correcting the performance problem; and generating a recommendation to execute the remedial measures for correcting the performance problem when unexpected metrics matches the set of unexpected metrics.
 11. A non-transitory computer-readable medium encoded with machine-readable instructions that implement a method carried out by one or more processors of a computer system to perform the operations comprising: providing a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem; in response to the user selecting the selected metric, identifying unexpected metrics of an object topology of the data center that are correlated with the selected metric in a recent time interval and are uncorrelated with the selected metric in a historical time interval that precedes the recent time interval and corresponds to when the object did not exhibit the performance problem; and generating a recommendation to correct the performance problem based on the unexpected metrics.
 12. The medium of claim 11 wherein identify unexpected metrics of the object topology of the data center comprises: computing correlation coefficients for the selected metric and metrics of objects of the object topology in the recent time interval; discarding metrics of objects of the object topology that are correlated with the selected metric in the historical time interval; and for each correlation coefficient when a correlation coefficient of a metric is greater than a correlation threshold, identifying the metric as an unexpected metric and recording the unexpected metric and associated correlation coefficient in an unexpected metric file, and rank ordering the unexpected metrics in the unexpected metric file.
 13. The medium of claim 12 wherein computing correlation coefficients for the selected metric and metrics of objects of the object topology in the recent time interval comprises: calculating correlation coefficients for the selected metric and metrics of the object associated with the selected metric; calculating correlation coefficients for the selected metric and metrics of the objects at the same level as the object in the object topology; calculating correlation coefficients for the selected metric and metrics of the objects in different levels of the object topology; and calculating correlation coefficients for the selected metric and metrics of user identified objects outside the object topology.
 14. The medium of claim 11 further comprising: providing a graphical user interface that enables the user to rate each unexpected metric; assigning a user rating to each unexpected metric; discarding unexpected metrics with corresponding user ratings that are less than a user-rating cutoff; rank ordering the unexpected metrics with user ratings that are greater than or equal to the user-rating cutoff; and annotating the unexpected metrics that user has associated with the performance problem.
 15. The medium of claim 11 further comprising: determining a frequency of occurrence for a set of unexpected metrics; identifying the performance problem associated with the set of unexpected metrics recording remedial measures for correcting the performance problem; and generating a recommendation to execute the remedial measures for correcting the performance problem when unexpected metrics matches the set of unexpected metrics. 